Integrating AIoT Technologies in Aquaculture: A Systematic Review

Fahmida Wazed Tina, Nasrin Afsarimanesh, Anindya Nag and Md Eshrat E. Alahi ()
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Fahmida Wazed Tina: Creative Innovation in Science and Technology Program, Faculty of Science and Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat 80280, Thailand
Nasrin Afsarimanesh: School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, Australia
Anindya Nag: Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
Md Eshrat E. Alahi: School of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala District, Nakhon Si Thammarat 80160, Thailand

Future Internet, 2025, vol. 17, issue 5, 1-35

Abstract: The increasing global demand for seafood underscores the necessity for sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and the threat of disease outbreaks, impede progress in this field. This review explores the transformative role of the Artificial Intelligence of Things (AIoT) in mitigating these challenges. We analyse current research on AIoT applications in aquaculture, with a strong emphasis on the use of IoT sensors for real-time data collection and AI algorithms for effective data analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, and employing automated counting techniques. Nevertheless, several research gaps remain, particularly regarding the integration of AI in broodstock management, the development of multimodal AI systems, and challenges regarding model generalization. Future advancements in AIoT should prioritise real-time adaptability, cost-effectiveness, and sustainability while emphasizing the importance of multimodal systems, advanced biosensing capabilities, and digital twin technologies. In conclusion, while AIoT presents substantial opportunities for enhancing aquaculture practices, successful implementation will depend on overcoming challenges related to scalability, cost, and technical expertise, improving models’ adaptability, and ensuring environmental sustainability.

Keywords: Artificial Intelligence of Things (AIoT); aquaculture; sustainable practices (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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